Overview

Dataset statistics

Number of variables14
Number of observations615
Missing cells31
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory67.4 KiB
Average record size in memory112.2 B

Variable types

Numeric12
Categorical2

Alerts

Unnamed: 0 is highly correlated with Category and 1 other fieldsHigh correlation
Category is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Sex is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
ALB is highly correlated with PROTHigh correlation
AST is highly correlated with Category and 1 other fieldsHigh correlation
CREA is highly correlated with SexHigh correlation
GGT is highly correlated with ASTHigh correlation
PROT is highly correlated with ALBHigh correlation
Unnamed: 0 is highly correlated with Category and 1 other fieldsHigh correlation
Category is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Sex is highly correlated with Unnamed: 0High correlation
ALB is highly correlated with PROTHigh correlation
AST is highly correlated with CategoryHigh correlation
PROT is highly correlated with ALBHigh correlation
Unnamed: 0 is highly correlated with Category and 2 other fieldsHigh correlation
Category is highly correlated with Unnamed: 0 and 8 other fieldsHigh correlation
Age is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Sex is highly correlated with Unnamed: 0High correlation
ALB is highly correlated with Category and 1 other fieldsHigh correlation
ALP is highly correlated with ALT and 2 other fieldsHigh correlation
ALT is highly correlated with Category and 3 other fieldsHigh correlation
AST is highly correlated with Category and 3 other fieldsHigh correlation
BIL is highly correlated with AST and 3 other fieldsHigh correlation
CHE is highly correlated with Category and 2 other fieldsHigh correlation
CHOL is highly correlated with Category and 3 other fieldsHigh correlation
CREA is highly correlated with ALPHigh correlation
GGT is highly correlated with Category and 4 other fieldsHigh correlation
PROT is highly correlated with Category and 2 other fieldsHigh correlation
ALP has 18 (2.9%) missing values Missing
CHOL has 10 (1.6%) missing values Missing
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique

Reproduction

Analysis started2022-02-17 14:33:18.918518
Analysis finished2022-02-17 14:33:41.743773
Duration22.83 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct615
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308
Minimum1
Maximum615
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2022-02-17T11:33:41.892794image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31.7
Q1154.5
median308
Q3461.5
95-th percentile584.3
Maximum615
Range614
Interquartile range (IQR)307

Descriptive statistics

Standard deviation177.6794867
Coefficient of variation (CV)0.5768814504
Kurtosis-1.2
Mean308
Median Absolute Deviation (MAD)154
Skewness0
Sum189420
Variance31570
MonotonicityStrictly increasing
2022-02-17T11:33:42.016788image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.2%
4141
 
0.2%
4071
 
0.2%
4081
 
0.2%
4091
 
0.2%
4101
 
0.2%
4111
 
0.2%
4121
 
0.2%
4131
 
0.2%
4151
 
0.2%
Other values (605)605
98.4%
ValueCountFrequency (%)
11
0.2%
21
0.2%
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
91
0.2%
101
0.2%
ValueCountFrequency (%)
6151
0.2%
6141
0.2%
6131
0.2%
6121
0.2%
6111
0.2%
6101
0.2%
6091
0.2%
6081
0.2%
6071
0.2%
6061
0.2%

Category
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
533 
4
 
30
2
 
24
3
 
21
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters615
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0533
86.7%
430
 
4.9%
224
 
3.9%
321
 
3.4%
17
 
1.1%

Length

2022-02-17T11:33:42.132451image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-17T11:33:42.199451image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0533
86.7%
430
 
4.9%
224
 
3.9%
321
 
3.4%
17
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0533
86.7%
430
 
4.9%
224
 
3.9%
321
 
3.4%
17
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number615
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0533
86.7%
430
 
4.9%
224
 
3.9%
321
 
3.4%
17
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common615
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0533
86.7%
430
 
4.9%
224
 
3.9%
321
 
3.4%
17
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0533
86.7%
430
 
4.9%
224
 
3.9%
321
 
3.4%
17
 
1.1%

Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct49
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.40813008
Minimum19
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2022-02-17T11:33:42.294496image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile33
Q139
median47
Q354
95-th percentile64.3
Maximum77
Range58
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.05510545
Coefficient of variation (CV)0.2120966473
Kurtosis-0.3864733604
Mean47.40813008
Median Absolute Deviation (MAD)8
Skewness0.2671344916
Sum29156
Variance101.1051455
MonotonicityNot monotonic
2022-02-17T11:33:42.420522image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
4632
 
5.2%
4828
 
4.6%
3325
 
4.1%
5124
 
3.9%
5222
 
3.6%
5021
 
3.4%
4921
 
3.4%
3521
 
3.4%
3820
 
3.3%
5320
 
3.3%
Other values (39)381
62.0%
ValueCountFrequency (%)
191
 
0.2%
231
 
0.2%
251
 
0.2%
271
 
0.2%
292
 
0.3%
301
 
0.2%
3217
2.8%
3325
4.1%
3419
3.1%
3521
3.4%
ValueCountFrequency (%)
771
 
0.2%
762
 
0.3%
751
 
0.2%
742
 
0.3%
713
 
0.5%
703
 
0.5%
684
0.7%
673
 
0.5%
664
0.7%
658
1.3%

Sex
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0
377 
1
238 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters615
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0377
61.3%
1238
38.7%

Length

2022-02-17T11:33:42.561488image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-17T11:33:42.642490image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0377
61.3%
1238
38.7%

Most occurring characters

ValueCountFrequency (%)
0377
61.3%
1238
38.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number615
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0377
61.3%
1238
38.7%

Most occurring scripts

ValueCountFrequency (%)
Common615
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0377
61.3%
1238
38.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0377
61.3%
1238
38.7%

ALB
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct189
Distinct (%)30.8%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean41.62019544
Minimum14.9
Maximum82.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2022-02-17T11:33:42.730529image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum14.9
5-th percentile32.26
Q138.8
median41.95
Q345.2
95-th percentile48.935
Maximum82.2
Range67.3
Interquartile range (IQR)6.4

Descriptive statistics

Standard deviation5.780629404
Coefficient of variation (CV)0.138890011
Kurtosis5.983300564
Mean41.62019544
Median Absolute Deviation (MAD)3.15
Skewness-0.1767675891
Sum25554.8
Variance33.41567631
MonotonicityNot monotonic
2022-02-17T11:33:42.883730image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3914
 
2.3%
44.712
 
2.0%
4112
 
2.0%
39.912
 
2.0%
46.411
 
1.8%
4310
 
1.6%
43.49
 
1.5%
429
 
1.5%
41.29
 
1.5%
368
 
1.3%
Other values (179)508
82.6%
ValueCountFrequency (%)
14.91
0.2%
19.31
0.2%
201
0.2%
20.31
0.2%
21.61
0.2%
22.51
0.2%
232
0.3%
241
0.2%
24.91
0.2%
26.21
0.2%
ValueCountFrequency (%)
82.21
0.2%
62.91
0.2%
59.81
0.2%
59.71
0.2%
55.41
0.2%
54.41
0.2%
53.31
0.2%
531
0.2%
52.41
0.2%
52.21
0.2%

ALP
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct414
Distinct (%)69.3%
Missing18
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean68.2839196
Minimum11.3
Maximum416.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2022-02-17T11:33:43.044726image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum11.3
5-th percentile36.94
Q152.5
median66.2
Q380.1
95-th percentile104
Maximum416.6
Range405.3
Interquartile range (IQR)27.6

Descriptive statistics

Standard deviation26.0283153
Coefficient of variation (CV)0.3811778154
Kurtosis54.97290503
Mean68.2839196
Median Absolute Deviation (MAD)13.7
Skewness4.65492065
Sum40765.5
Variance677.4731974
MonotonicityNot monotonic
2022-02-17T11:33:43.188723image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.55
 
0.8%
61.25
 
0.8%
59.54
 
0.7%
84.14
 
0.7%
66.53
 
0.5%
58.93
 
0.5%
79.33
 
0.5%
1063
 
0.5%
70.33
 
0.5%
61.83
 
0.5%
Other values (404)561
91.2%
(Missing)18
 
2.9%
ValueCountFrequency (%)
11.31
0.2%
19.11
0.2%
20.61
0.2%
22.91
0.2%
26.91
0.2%
271
0.2%
27.31
0.2%
27.51
0.2%
28.91
0.2%
29.62
0.3%
ValueCountFrequency (%)
416.61
0.2%
208.21
0.2%
190.71
0.2%
1451
0.2%
143.11
0.2%
137.81
0.2%
137.21
0.2%
136.91
0.2%
1261
0.2%
1241
0.2%

ALT
Real number (ℝ≥0)

HIGH CORRELATION

Distinct341
Distinct (%)55.5%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean28.45081433
Minimum0.9
Maximum325.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2022-02-17T11:33:43.338737image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile8.495
Q116.4
median23
Q333.075
95-th percentile62.035
Maximum325.3
Range324.4
Interquartile range (IQR)16.675

Descriptive statistics

Standard deviation25.46968881
Coefficient of variation (CV)0.895218271
Kurtosis47.12926134
Mean28.45081433
Median Absolute Deviation (MAD)7.6
Skewness5.506113537
Sum17468.8
Variance648.7050483
MonotonicityNot monotonic
2022-02-17T11:33:43.479187image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.67
 
1.1%
18.66
 
1.0%
19.96
 
1.0%
23.85
 
0.8%
21.45
 
0.8%
235
 
0.8%
19.75
 
0.8%
17.25
 
0.8%
25.25
 
0.8%
18.35
 
0.8%
Other values (331)560
91.1%
ValueCountFrequency (%)
0.91
0.2%
1.21
0.2%
1.31
0.2%
2.11
0.2%
2.31
0.2%
2.41
0.2%
2.51
0.2%
2.91
0.2%
3.51
0.2%
3.71
0.2%
ValueCountFrequency (%)
325.31
0.2%
2581
0.2%
208.81
0.2%
1641
0.2%
1591
0.2%
1301
0.2%
118.11
0.2%
1181
0.2%
1142
0.3%
103.61
0.2%

AST
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct297
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.78634146
Minimum10.6
Maximum324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2022-02-17T11:33:43.627187image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile16.67
Q121.6
median25.9
Q332.9
95-th percentile91.84
Maximum324
Range313.4
Interquartile range (IQR)11.3

Descriptive statistics

Standard deviation33.09069034
Coefficient of variation (CV)0.9512552613
Kurtosis30.83664076
Mean34.78634146
Median Absolute Deviation (MAD)5.2
Skewness4.940326986
Sum21393.6
Variance1094.993787
MonotonicityNot monotonic
2022-02-17T11:33:43.864251image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.97
 
1.1%
227
 
1.1%
24.37
 
1.1%
21.26
 
1.0%
25.76
 
1.0%
19.26
 
1.0%
22.16
 
1.0%
19.46
 
1.0%
24.76
 
1.0%
17.56
 
1.0%
Other values (287)552
89.8%
ValueCountFrequency (%)
10.61
0.2%
121
0.2%
12.21
0.2%
13.11
0.2%
13.31
0.2%
14.12
0.3%
14.71
0.2%
14.81
0.2%
14.91
0.2%
152
0.3%
ValueCountFrequency (%)
3241
0.2%
319.81
0.2%
285.81
0.2%
263.11
0.2%
188.71
0.2%
187.91
0.2%
187.71
0.2%
1851
0.2%
181.81
0.2%
164.21
0.2%

BIL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct188
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.39674797
Minimum0.8
Maximum254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2022-02-17T11:33:44.010287image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile3
Q15.3
median7.3
Q311.2
95-th percentile24.03
Maximum254
Range253.2
Interquartile range (IQR)5.9

Descriptive statistics

Standard deviation19.67314981
Coefficient of variation (CV)1.726207324
Kurtosis83.18673191
Mean11.39674797
Median Absolute Deviation (MAD)2.7
Skewness8.385436708
Sum7009
Variance387.0328233
MonotonicityNot monotonic
2022-02-17T11:33:44.153287image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
613
 
2.1%
712
 
2.0%
6.911
 
1.8%
5.711
 
1.8%
4.111
 
1.8%
6.111
 
1.8%
6.310
 
1.6%
3.710
 
1.6%
6.810
 
1.6%
5.59
 
1.5%
Other values (178)507
82.4%
ValueCountFrequency (%)
0.81
 
0.2%
1.81
 
0.2%
21
 
0.2%
2.11
 
0.2%
2.22
 
0.3%
2.32
 
0.3%
2.46
1.0%
2.63
0.5%
2.73
0.5%
2.84
0.7%
ValueCountFrequency (%)
2541
0.2%
2091
0.2%
2002
0.3%
1191
0.2%
1171
0.2%
911
0.2%
671
0.2%
59.11
0.2%
581
0.2%
501
0.2%

CHE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct407
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.196634146
Minimum1.42
Maximum16.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2022-02-17T11:33:44.288286image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1.42
5-th percentile4.541
Q16.935
median8.26
Q39.59
95-th percentile11.362
Maximum16.41
Range14.99
Interquartile range (IQR)2.655

Descriptive statistics

Standard deviation2.20565727
Coefficient of variation (CV)0.2690930486
Kurtosis1.314730096
Mean8.196634146
Median Absolute Deviation (MAD)1.33
Skewness-0.1102327104
Sum5040.93
Variance4.864923995
MonotonicityNot monotonic
2022-02-17T11:33:44.438255image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.526
 
1.0%
9.825
 
0.8%
7.15
 
0.8%
5.955
 
0.8%
6.84
 
0.7%
8.844
 
0.7%
7.934
 
0.7%
7.54
 
0.7%
74
 
0.7%
8.94
 
0.7%
Other values (397)570
92.7%
ValueCountFrequency (%)
1.421
0.2%
1.481
0.2%
1.541
0.2%
1.571
0.2%
1.661
0.2%
1.721
0.2%
1.731
0.2%
1.82
0.3%
1.881
0.2%
21
0.2%
ValueCountFrequency (%)
16.411
0.2%
15.431
0.2%
15.42
0.3%
15.11
0.2%
14.81
0.2%
13.861
0.2%
13.82
0.3%
13.711
0.2%
13.31
0.2%
12.861
0.2%

CHOL
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct313
Distinct (%)51.7%
Missing10
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean5.368099174
Minimum1.43
Maximum9.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2022-02-17T11:33:44.588286image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1.43
5-th percentile3.622
Q14.61
median5.3
Q36.06
95-th percentile7.29
Maximum9.67
Range8.24
Interquartile range (IQR)1.45

Descriptive statistics

Standard deviation1.132728431
Coefficient of variation (CV)0.211011085
Kurtosis0.694022894
Mean5.368099174
Median Absolute Deviation (MAD)0.73
Skewness0.3758275548
Sum3247.7
Variance1.283073699
MonotonicityNot monotonic
2022-02-17T11:33:44.722286image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.18
 
1.3%
5.078
 
1.3%
5.37
 
1.1%
5.736
 
1.0%
5.96
 
1.0%
5.885
 
0.8%
4.435
 
0.8%
4.695
 
0.8%
5.425
 
0.8%
4.685
 
0.8%
Other values (303)545
88.6%
(Missing)10
 
1.6%
ValueCountFrequency (%)
1.431
0.2%
2.41
0.2%
2.611
0.2%
2.791
0.2%
2.861
0.2%
3.011
0.2%
3.021
0.2%
3.092
0.3%
3.11
0.2%
3.191
0.2%
ValueCountFrequency (%)
9.671
0.2%
9.431
0.2%
9.031
0.2%
8.891
0.2%
8.81
0.2%
8.781
0.2%
8.61
0.2%
8.461
0.2%
8.361
0.2%
8.281
0.2%

CREA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct117
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.28780488
Minimum8
Maximum1079.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2022-02-17T11:33:44.858255image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile55.55
Q167
median77
Q388
95-th percentile106
Maximum1079.1
Range1071.1
Interquartile range (IQR)21

Descriptive statistics

Standard deviation49.75616601
Coefficient of variation (CV)0.6120987778
Kurtosis280.1002374
Mean81.28780488
Median Absolute Deviation (MAD)10
Skewness15.16929115
Sum49992
Variance2475.676056
MonotonicityNot monotonic
2022-02-17T11:33:45.031258image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7421
 
3.4%
7020
 
3.3%
7219
 
3.1%
6718
 
2.9%
8317
 
2.8%
6416
 
2.6%
8816
 
2.6%
7815
 
2.4%
6915
 
2.4%
7115
 
2.4%
Other values (107)443
72.0%
ValueCountFrequency (%)
81
0.2%
91
0.2%
291
0.2%
321
0.2%
401
0.2%
411
0.2%
45.41
0.2%
481
0.2%
49.61
0.2%
502
0.3%
ValueCountFrequency (%)
1079.11
0.2%
5191
0.2%
485.91
0.2%
1701
0.2%
158.21
0.2%
147.31
0.2%
136.11
0.2%
1271
0.2%
1191
0.2%
118.21
0.2%

GGT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct358
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.53317073
Minimum4.5
Maximum650.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2022-02-17T11:33:45.171288image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile10.27
Q115.7
median23.3
Q340.2
95-th percentile108.5
Maximum650.9
Range646.4
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation54.66107124
Coefficient of variation (CV)1.382663475
Kurtosis43.71257909
Mean39.53317073
Median Absolute Deviation (MAD)9.3
Skewness5.632734058
Sum24312.9
Variance2987.832709
MonotonicityNot monotonic
2022-02-17T11:33:45.316256image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.16
 
1.0%
136
 
1.0%
14.56
 
1.0%
24.16
 
1.0%
15.45
 
0.8%
11.45
 
0.8%
15.95
 
0.8%
12.35
 
0.8%
17.45
 
0.8%
20.85
 
0.8%
Other values (348)561
91.2%
ValueCountFrequency (%)
4.51
 
0.2%
4.91
 
0.2%
6.41
 
0.2%
72
0.3%
7.11
 
0.2%
7.21
 
0.2%
7.41
 
0.2%
7.61
 
0.2%
7.93
0.5%
81
 
0.2%
ValueCountFrequency (%)
650.91
0.2%
4911
0.2%
400.31
0.2%
399.51
0.2%
392.21
0.2%
345.61
0.2%
295.61
0.2%
273.71
0.2%
2391
0.2%
218.31
0.2%

PROT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct198
Distinct (%)32.2%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean72.04413681
Minimum44.8
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2022-02-17T11:33:45.477762image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum44.8
5-th percentile64.1
Q169.3
median72.2
Q375.4
95-th percentile80.04
Maximum90
Range45.2
Interquartile range (IQR)6.1

Descriptive statistics

Standard deviation5.402635737
Coefficient of variation (CV)0.07499063736
Kurtosis3.544529196
Mean72.04413681
Median Absolute Deviation (MAD)3
Skewness-0.9636873886
Sum44235.1
Variance29.18847291
MonotonicityNot monotonic
2022-02-17T11:33:45.645308image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.915
 
2.4%
73.113
 
2.1%
729
 
1.5%
72.49
 
1.5%
69.99
 
1.5%
71.88
 
1.3%
71.38
 
1.3%
70.58
 
1.3%
75.28
 
1.3%
77.37
 
1.1%
Other values (188)520
84.6%
ValueCountFrequency (%)
44.81
0.2%
471
0.2%
47.82
0.3%
511
0.2%
53.11
0.2%
53.21
0.2%
54.21
0.2%
56.31
0.2%
56.92
0.3%
571
0.2%
ValueCountFrequency (%)
901
0.2%
86.51
0.2%
861
0.2%
841
0.2%
83.41
0.2%
83.31
0.2%
82.71
0.2%
82.61
0.2%
82.41
0.2%
82.31
0.2%

Interactions

2022-02-17T11:33:39.592389image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:24.440347image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:25.925963image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:27.217963image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:28.668479image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:30.064930image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:31.470242image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:32.744241image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:34.187489image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:35.538724image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:36.847744image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:38.122947image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:39.702391image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:24.619987image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:26.038964image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:27.329966image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:28.791532image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:30.170915image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:31.575275image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:32.859275image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:34.309498image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:35.642727image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:36.958726image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:38.240541image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:39.807935image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:24.805968image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:26.152963image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:27.435965image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:28.907482image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:30.276918image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:31.681242image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:32.965243image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:34.431485image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:35.744725image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:37.060758image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:38.355541image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:39.910459image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:24.918955image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:26.258965image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:27.643963image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:29.023479image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:30.477241image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:31.788244image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:33.181002image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:34.549495image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:35.938735image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:37.160724image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:38.472169image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:40.024460image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:25.040967image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:26.370975image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:27.756963image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:29.149511image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:30.585275image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:31.903275image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:33.296945image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:34.672461image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:36.046724image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:37.274725image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:38.694147image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:40.128504image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:25.155967image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:26.481963image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:27.874965image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:29.267503image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:30.693280image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:32.005277image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:33.405945image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:34.790462image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:36.146751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:37.381728image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:38.812322image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:40.229577image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:25.269972image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:26.589999image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:27.985970image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:29.386513image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:30.823277image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:32.112243image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:33.518947image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:34.900462image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:36.244728image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:37.483739image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:38.926358image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:40.329597image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:25.376964image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:26.690964image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:28.097967image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:29.498494image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:30.932244image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:32.215248image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:33.636945image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:35.005461image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:36.340724image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:37.584736image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:39.034323image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:40.441639image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:25.489966image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:26.802963image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:28.218480image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:29.625496image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:31.042276image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:32.326252image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:33.754461image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:35.116481image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:36.450725image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:37.692741image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:39.155322image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:40.539639image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:25.596998image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:26.909963image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:28.324480image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:29.736528image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:31.151245image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:32.428242image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:33.860465image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:35.218727image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:36.547741image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:37.793770image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:39.259323image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:40.642639image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:25.706990image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:27.012963image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:28.440480image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:29.851496image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:31.256241image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:32.538254image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:33.971461image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:35.323728image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:36.646725image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:37.903737image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:39.371327image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:40.750702image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:25.819965image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:27.118985image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:28.558481image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:29.961494image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:31.364240image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:32.642276image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:34.083460image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:35.430733image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:36.750727image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:38.018777image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-17T11:33:39.486323image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2022-02-17T11:33:45.786662image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-17T11:33:46.062665image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-17T11:33:46.227627image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-17T11:33:46.384649image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-17T11:33:46.536614image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-17T11:33:40.955807image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-17T11:33:41.241918image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-17T11:33:41.427592image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-17T11:33:41.628788image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0CategoryAgeSexALBALPALTASTBILCHECHOLCREAGGTPROT
01032038.552.57.722.17.56.933.23106.012.169.0
12032038.570.318.024.73.911.174.8074.015.676.5
23032046.974.736.252.66.18.845.2086.033.279.3
34032043.252.030.622.618.97.334.7480.033.875.7
45032039.274.132.624.89.69.154.3276.029.968.7
56032041.643.318.519.712.39.926.05111.091.074.0
67032046.341.317.517.88.57.014.7970.016.974.5
78032042.241.935.831.116.15.824.60109.021.567.1
89032050.965.523.221.26.98.694.1083.013.771.3
910032042.486.320.320.035.25.464.4581.015.969.9

Last rows

Unnamed: 0CategoryAgeSexALBALPALTASTBILCHECHOLCREAGGTPROT
605606442133.079.03.755.7200.01.725.1689.1146.369.9
606607449133.0190.71.236.37.06.923.82485.9112.058.5
607608452139.037.01.330.421.06.333.78158.2142.582.7
608609458134.046.415.0150.08.06.263.9856.049.780.6
609610459139.051.319.6285.840.05.774.51136.1101.170.5
610611462132.0416.65.9110.350.05.576.3055.7650.968.5
611612464124.0102.82.944.420.01.543.0263.035.971.3
612613464129.087.33.599.048.01.663.6366.764.282.0
613614446133.0NaN39.062.020.03.564.2052.050.071.0
614615459136.0NaN100.080.012.09.075.3067.034.068.0